Self-Supervised Learning Powered by Synthetic Data From Diffusion Models: Application to X-Ray Images

Synthetic data offers a compelling solution to the challenges associated with acquiring high-quality medical data, which is often constrained by privacy concerns and limited accessibility. This study explores the efficacy of synthetic data generated using diffusion models for training deep learning models within a self-supervised learning framework. The primary objective is to evaluate whether synthetic data can effectively preserve critical medical biomarkers and support reliable downstream tasks such as classification and segmentation. Using chest X-ray images as a case study, the results reveal that models pretrained on synthetic data achieve performance comparable to or surpassing those pretrained on real data. Specifically, in pneumonia classification task, the model trained on synthetic data outperformed established benchmarks, achieving an Area Under the Curve of 99.1 and an F1-score of 96.1%. Similarly, for segmentation tasks, the model trained on synthetic data demonstrated robust performance, attaining a Dice score of 0.85. These findings underscore a significant advancement in the generation of synthetic medical images, providing a viable approach to creating realistic, biomarker-preserving datasets that ensure patient confidentiality and enable diverse applications in medical imaging.